NETWORK DYNAMICS AND SYNCHRONOUS ACTIVITY IN CULTURED CORTICAL NEURONS

MiR-186-5p inhibition restores synaptic transmission and neuronal network activity in a model of chronic stress

Chronic stress exerts profound negative effects on cognitive and emotional behaviours and is a major risk factor for the development of neuropsychiatric disorders. However, the molecular links between chronic stress and its deleterious effects on neuronal and synaptic function remain elusive. Here, using a combination of in vitro and in vivo approaches, we demonstrate that the upregulation of miR-186-5p triggered by chronic stress may be a key mediator of such changes, leading to synaptic dysfunction. Our results show that the expression levels of miR-186-5p are increased both in the prefrontal cortex (PFC) of mice exposed to chronic stress and in cortical neurons chronically exposed to dexamethasone. Additionally, viral overexpression of miR-186-5p in the PFC of naïve mice induces anxiety- and depressive-like behaviours. The upregulation of miR-186-5p through prolonged glucocorticoid receptor activation in vitro, or in a mouse model of chronic stress, differentially affects glutamatergic and GABAergic synaptic transmission, causing an imbalance in excitation/inhibition that leads to altered neuronal network activity. At glutamatergic synapses, we observed both a reduction in synaptic AMPARs and synaptic transmission, whereas GABAergic synaptic transmission was strengthened. These changes could be rescued in vitro by a miR-186-5p inhibitor. Overall, our results establish a novel molecular link between chronic glucocorticoid receptor activation, the upregulation of miR-186-5p and the synaptic changes induced by chronic stress, that may be amenable to therapeutic intervention.

Modeling seizure networks in neuron-glia cultures using microelectrode arrays

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 % of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks in vitro. As such, we sought to develop a novel in vitro MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2 days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

Modeling mTORopathy-related epilepsy in cultured murine hippocampal neurons using the multi-electrode array

The mechanistic target of rapamycin complex 1 (mTORC1) signaling pathway is a ubiquitous cellular pathway. mTORopathies, a group of disorders characterized by hyperactivity of the mTORC1 pathway, illustrate the prominent role of the mTOR pathway in disease pathology, often profoundly affecting the central nervous system. One of the most debilitating symptoms of mTORopathies is drug-resistant epilepsy, emphasizing the urgent need for a deeper understanding of disease mechanisms to develop novel anti-epileptic drugs. In this study, we explored the multiwell Multi-electrode array (MEA) system as a tool to identify robust network activity parameters in an approach to model mTORopathy-related epilepsy in vitro. To this extent, we cultured mouse primary hippocampal neurons on the multiwell MEA to identify robust network activity phenotypes in mTORC1-hyperactive neuronal networks. mTOR-hyperactivity was induced either through deletion of Tsc1 or overexpression of a constitutively active RHEB variant identified in patients, RHEBp.P37L. mTORC1 dependency of the phenotypes was assessed using rapamycin, and vigabatrin was applied to treat epilepsy-like phenotypes. We show that hyperactivity of the mTORC1 pathway leads to aberrant network activity. In both the Tsc1-KO and RHEB-p.P37L models, we identified changes in network synchronicity, rhythmicity, and burst characteristics. The presence of these phenotypes is prevented upon early treatment with the mTORC1-inhibitor rapamycin. Application of rapamycin in mature neuronal cultures could only partially rescue the network activity phenotypes. Additionally, treatment with the anti-epileptic drug vigabatrin reduced network activity and restored burst characteristics. Taken together, we showed that mTORC1-hyperactive neuronal cultures on the multiwell MEA system present reliable network activity phenotypes that can be used as an assay to explore the potency of new drug treatments targeting epilepsy in mTORopathy patients and may give more insights into the pathophysiological mechanisms underlying epilepsy in these patients.

Neuromodulatory Compensation of Cortical Neural Activity on Electrodeposited Pt/Ir Modified Microelectrode Arrays for Temperature Transients

Temperature has a profound influence on various neuromodulation processes and has emerged as a focal point. However, the effects of acute environmental temperature fluctuations on cultured cortical networks have been inadequately elucidated. To bridge this gap, we have developed a brain-on-a-chip platform integrating cortical networks and electrodeposited Pt/Ir modified microelectrode arrays (MEAs) with 3D-printed bear-shaped triple chambers, facilitating control of temperature transients. This innovative system administers thermal stimuli while concurrently monitoring neuronal activity, including spikes and local field potentials, from 60 microelectrodes (diameter: 30 μm; impedance: 9.34 ± 1.37 kΩ; and phase delay: -45.26 ± 2.85°). Temperature transitions of approximately ±10 °C/s were applied to cortical networks on MEAs via in situ perfusion within the triple chambers. Subsequently, we examined the spatiotemporal dynamics of the brain-on-a-chip under temperature regulation at both the group level (neuronal population) and their interactions (network dynamics) and the individual level (cellular activity). Specifically, we found that after the temperature reduction neurons enhanced the overall information transmission efficiency of the network through synchronous firing to compensate for the decreased efficiency of single-cell level information transmission, in contrast to temperature elevation. By leveraging the integration of high-performance MEAs with perfusion chambers, this investigation provides a comprehensive understanding of the impact of temperature on the spatiotemporal dynamics of neural networks, thereby facilitating future exploration of the intricate interplay between temperature and brain function.

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

Time-dependent homeostatic mechanisms underlie brain-derived neurotrophic factor action on neural circuitry

Plasticity and homeostatic mechanisms allow neural networks to maintain proper function while responding to physiological challenges. Despite previous work investigating morphological and synaptic effects of brain-derived neurotrophic factor (BDNF), the most prevalent growth factor in the central nervous system, how exposure to BDNF manifests at the network level remains unknown. Here we report that BDNF treatment affects rodent hippocampal network dynamics during development and recovery from glutamate-induced excitotoxicity in culture. Importantly, these effects are not obvious when traditional activity metrics are used, so we delve more deeply into network organization, functional analyses, and in silico simulations. We demonstrate that BDNF partially restores homeostasis by promoting recovery of weak and medium connections after injury. Imaging and computational analyses suggest these effects are caused by changes to inhibitory neurons and connections. From our in silico simulations, we find that BDNF remodels the network by indirectly strengthening weak excitatory synapses after injury. Ultimately, our findings may explain the difficulties encountered in preclinical and clinical trials with BDNF and also offer information for future trials to consider.

Emerging Activity Patterns and Synaptogenesis in Dissociated Hippocampal Cultures

Cultures of dissociated hippocampal neurons display a stereotypical development of network activity patterns within the first three weeks of maturation. During this process, network connections develop and the associated spiking patterns range from increasing levels of activity in the first two weeks to regular bursting activity in the third week of maturation. Characterization of network structure is important to examine the mechanisms underlying the emergent functional organization of neural circuits. To accomplish this, confocal microscopy techniques have been used and several automated synapse quantification algorithms based on (co)localization of synaptic structures have been proposed recently. However, these approaches suffer from the arbitrary nature of intensity thresholding and the lack of correction for random-chance colocalization. To address this problem, we developed and validated an automated synapse quantification algorithm that requires minimal operator intervention. Next, we applied our approach to quantify excitatory and inhibitory synaptogenesis using confocal images of dissociated hippocampal neuronal cultures captured at 5, 8, 14 and 20 days in vitro, the time period associated with the development of distinct neuronal activity patterns. As expected, we found that synaptic density increased with maturation, coinciding with increasing spiking activity in the network. Interestingly, the third week of the maturation exhibited a reduction in excitatory synaptic density suggestive of synaptic pruning that coincided with the emergence of regular bursting activity in the network.

Loss of neuron network coherence induced by virus-infected astrocytes: a model study

Coherent activations of brain neuron networks underlie many physiological functions associated with various behavioral states. These synchronous fluctuations in the electrical activity of the brain are also referred to as brain rhythms. At the cellular level, rhythmicity can be induced by various mechanisms of intrinsic oscillations in neurons or the network circulation of excitation between synaptically coupled neurons. One specific mechanism concerns the activity of brain astrocytes that accompany neurons and can coherently modulate synaptic contacts of neighboring neurons, synchronizing their activity. Recent studies have shown that coronavirus infection (Covid-19), which enters the central nervous system and infects astrocytes, can cause various metabolic disorders. Specifically, Covid-19 can depress the synthesis of astrocytic glutamate and gamma-aminobutyric acid. It is also known that in the post-Covid state, patients may suffer from symptoms of anxiety and impaired cognitive functions. We propose a mathematical model of a spiking neuron network accompanied by astrocytes capable of generating quasi-synchronous rhythmic bursting discharges. The model predicts that if the release of glutamate is depressed, normal burst rhythmicity will suffer dramatically. Interestingly, in some cases, the failure of network coherence may be intermittent, with intervals of normal rhythmicity, or the synchronization can disappear.

Recent Progress and Perspectives on Neural Chip Platforms Integrating PDMS-Based Microfluidic Devices and Microelectrode Arrays

Recent years have witnessed a spurt of progress in the application of the encoding and decoding of neural activities to drug screening, diseases diagnosis, and brain-computer interactions. To overcome the constraints of the complexity of the brain and the ethical considerations of in vivo research, neural chip platforms integrating microfluidic devices and microelectrode arrays have been raised, which can not only customize growth paths for neurons in vitro but also monitor and modulate the specialized neural networks grown on chips. Therefore, this article reviews the developmental history of chip platforms integrating microfluidic devices and microelectrode arrays. First, we review the design and application of advanced microelectrode arrays and microfluidic devices. After, we introduce the fabrication process of neural chip platforms. Finally, we highlight the recent progress on this type of chip platform as a research tool in the field of brain science and neuroscience, focusing on neuropharmacology, neurological diseases, and simplified brain models. This is a detailed and comprehensive review of neural chip platforms. This work aims to fulfill the following three goals: (1) summarize the latest design patterns and fabrication schemes of such platforms, providing a reference for the development of other new platforms; (2) generalize several important applications of chip platforms in the field of neurology, which will attract the attention of scientists in the field; and (3) propose the developmental direction of neural chip platforms integrating microfluidic devices and microelectrode arrays.

Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks

Neurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.

Is the forming of neuronal network activity in human-induced pluripotent stem cells important for the detection of drug-induced seizure risks?

BACKGROUND

Functional network activity is a characteristic for neuronal cells, and the complexity of the network activity represents the necessary substrate to support complex brain functions. Drugs that drastically increase the neuronal network activity may have a potential higher risk for seizures in human. Although there has been some recent considerable progress made using cultures from different types of human-induced pluripotent stem cell (hiPSC) derived neurons, one of the primary limitations is the lack of - or very low - network activity.

METHOD

In the present study, we investigated whether the limited neuronal network activity in commercial hiPSC-neurons (CNS.4U®) is capable of detecting drug-induced potential seizure risks. Therefore, we compared the hiPSC-results to those in rat primary neurons with known high neuronal network activity in vitro.

RESULTS

Gene expression and electrical activity from in vitro developing neuronal networks were assessed at multiple time-points. Transcriptomes of 7, 28, and 50 days in vitro were analyzed and compared to those from human brain tissues. Data from measurements of electrical activity using multielectrode arrays (MEAs) indicate that neuronal networks matured gradually over time, albeit in hiPSC this developed slower than rat primary cultures. The response of neuronal networks to neuronal active reference drugs modulating glutamatergic, acetylcholinergic and GABAergic pathways could be detected in both hiPSC-neurons and rat primary neurons. However, in comparison, GABAergic responses were limited in hiPSC-neurons.

CONCLUSION

Overall, despite a slower network development and lower network activity, CNS.4U® hiPSC-neurons can be used to detect drug induced changes in neuronal network activity, as shown by well-known seizurogenic drugs (affecting e.g., the Glycine receptor and Na⁺ channel). However, lower sensitivity to GABA antagonists has been observed.

Automatic differentiation of human induced pluripotent stem cells toward synchronous neural networks on an arrayed monolayer of nanofiber membrane

Automatic differentiation of human-induced pluripotent stem cells (hiPSCs) facilitates the generation of cortical neural networks and studies of brain functions. Here, we present a method of directed differentiation of hiPSCs with a substrate made of a honeycomb microframe and a monolayer of crosslinked gelatin nanofibers in the form of an array of nanofiber membranes. Neural precursor cells (NPCs) were firstly derived from hiPSCs and then placed on the nanofiber membranes for automatically controlled neural differentiation over a long period. Due to the strong modulation of the substrate stiffness and permeability, most cells were found in the center area of the honeycomb compartments, giving rise to regular and inter-connected cortical neural clusters. More importantly, the neural activities of the clusters were synchronized proving the reliability of the method. Our results showed that the self-organization, as well as the neural activities of differentiating neural cells, were more efficient in the nanofiber membrane compared to the types of the substrate such as glass and nanofiber-covered glass. In addition to the inherent advantages such as manpower saving and fewer risks of contamination and human error, automatic differentiation avoided undesired shaking which might have critical effects on the formation of synchronous neural clusters. STATEMENT OF SIGNIFICANCE: : Synchronization of cortical neural activities is essential for information processing and human cognition. By automated differentiation of human induced pluripotent stem cells on arrayed monolayer of nanofiber membrane, synchronous neural clusters could be formed. Such an approach would allow creating a variety of neural networks with regular and interconnected clusters for systematic studies of human cortical functions.

Tracking connectivity maps in human stem cell-derived neuronal networks by holographic optogenetics

Holographic optogenetic stimulation of human iPSC–derived neuronal networks was exploited to map precise functional connectivity motifs and their long-term dynamics during network development.

Neuronal networks derived from human induced pluripotent stem cells have been exploited widely for modeling neuronal circuits, neurological diseases, and drug screening. As these networks require extended culturing periods to functionally mature in vitro, most studies are based on immature networks. To obtain insights on long-term functional features, we improved a glia–neuron co-culture protocol within multi-electrode arrays, facilitating continuous assessment of electrical features in weekly intervals. By full-field optogenetic stimulation, we detected an earlier onset of neuronal firing and burst activity compared with spontaneous activity. Full-field stimulation enhanced the number of active neurons and their firing rates. Compared with full-field stimulation, which evoked synchronized activity across all neurons, holographic stimulation of individual neurons resulted in local activity. Single-cell holographic stimulation facilitated to trace propagating evoked activities of 400 individually stimulated neurons per multi-electrode array. Thereby, we revealed precise functional neuronal connectivity motifs. Holographic stimulation data over time showed increasing connection numbers and strength with culture age. This holographic stimulation setup has the potential to establish a profound functional testbed for in-depth analysis of human-induced pluripotent stem cell-derived neuronal networks.

Kuramoto Model-Based Analysis Reveals Oxytocin Effects on Brain Network Dynamics

The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, these studies are mainly based on the statistical correlation or Bayesian causality inference, lacking interpretability at the physical and neuroscience level. Here, we propose a physics-based framework of the Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN. Testing on fMRI data of 59 participants administrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the FPN and lower synchronization in the DMN, as well as a higher variance of the coupling strength within the DMN and more flexible coupling patterns at group level. These results together indicate that oxytocin may increase the ability to overcome the corresponding internal oscillation dispersion and support the flexibility in neural synchrony in various social contexts, providing new evidence for explaining the oxytocin modulated social behaviors. Our proposed Kuramoto model-based framework can be a potential tool in network neuroscience and offers physical and neural insights into phase dynamics of the brain.

Functionalized Nanocellulose Drives Neural Stem Cells toward Neuronal Differentiation

Transplantation of differentiated and fully functional neurons may be a better therapeutic option for the cure of neurodegenerative disorders and brain injuries than direct grafting of neural stem cells (NSCs) that are potentially tumorigenic. However, the differentiation of NSCs into a large population of neurons has been a challenge. Nanomaterials have been widely used as substrates to manipulate cell behavior due to their nano-size, excellent physicochemical properties, ease of synthesis, and versatility in surface functionalization. Nanomaterial-based scaffolds and synthetic polymers have been fabricated with topology resembling the micro-environment of the extracellular matrix. Nanocellulose materials are gaining attention because of their availability, biocompatibility, biodegradability and bioactivity, and affordable cost. We evaluated the role of nanocellulose with different linkage and surface features in promoting neuronal differentiation. Nanocellulose coupled with lysine molecules (CNC-Lys) provided positive charges that helped the cells to attach. Embryonic rat NSCs were differentiated on the CNC-Lys surface for up to three weeks. By the end of the three weeks of in vitro culture, 87% of the cells had attached to the CNC-Lys surface and more than half of the NSCs had differentiated into functional neurons, expressing endogenous glutamate, generating electrical activity and action potentials recorded by the multi-electrode array.

Spontaneous and Perturbational Complexity in Cortical Cultures

Dissociated cortical neurons in vitro display spontaneously synchronized, low-frequency firing patterns, which can resemble the slow wave oscillations characterizing sleep in vivo. Experiments in humans, rodents, and cortical slices have shown that awakening or the administration of activating neuromodulators decrease slow waves, while increasing the spatio-temporal complexity of responses to perturbations. In this study, we attempted to replicate those findings using in vitro cortical cultures coupled with micro-electrode arrays and chemically treated with carbachol (CCh), to modulate sleep-like activity and suppress slow oscillations. We adapted metrics such as neural complexity (NC) and the perturbational complexity index (PCI), typically employed in animal and human brain studies, to quantify complexity in simplified, unstructured networks, both during resting state and in response to electrical stimulation. After CCh administration, we found a decrease in the amplitude of the initial response and a marked enhancement of the complexity during spontaneous activity. Crucially, unlike in cortical slices and intact brains, PCI in cortical cultures displayed only a moderate increase. This dissociation suggests that PCI, a measure of the complexity of causal interactions, requires more than activating neuromodulation and that additional factors, such as an appropriate circuit architecture, may be necessary. Exploring more structured in vitro networks, characterized by the presence of strong lateral connections, recurrent excitation, and feedback loops, may thus help to identify the features that are more relevant to support causal complexity.

Early prediction of developing spontaneous activity in cultured neuronal networks

Synchronization and bursting activity are intrinsic electrophysiological properties of in vivo and in vitro neural networks. During early development, cortical cultures exhibit a wide repertoire of synchronous bursting dynamics whose characterization may help to understand the parameters governing the transition from immature to mature networks. Here we used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro. Network activity at three stages of early development was defined by 18 electrophysiological features of spikes, bursts, synchrony, and connectivity. The variability of neuronal network activity during early development was investigated by applying k-means and self-organizing map (SOM) clustering analysis to features of bursts and synchrony. These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest. Our results indicate that initial patterns of electrical activity during the first week in vitro may already predetermine the final development of the neuronal network activity. The methodological approach used here may be applied to explore the biological mechanisms underlying the complex dynamics of spontaneous activity in developing neuronal cultures.

Modulation of dynamics in a pre-existing hippocampal network by neural stem cells on a microelectrode array

Objective.Neural stem cells (NSCs) are continuously produced throughout life in the hippocampus, which is a vital structure for learning and memory. NSCs in the brain incorporate into the functional hippocampal circuits and contribute to processing information. However, little is known about the mechanisms of NSCs' activity in a pre-existing neuronal network. Here, we investigate the role of NSCs in the neuronal activity of a pre-existing hippocampalin vitronetwork grown on microelectrode arrays.Approach.We assessed the change in internal dynamics of the network by additional NSCs based on spontaneous activity. We also evaluated the networks' ability to discriminate between different input patterns by measuring evoked activity in response to external inputs.Main results.Analysis of spontaneous activity revealed that additional NSCs prolonged network bursts with longer intervals, generated a lower number of initiating patterns, and decreased synchronization among neurons. Moreover, the network with NSCs showed higher synchronicity in close connections among neurons responding to external inputs and a larger difference in spike counts and cross-correlations during evoked response between two different inputs. Taken together, our results suggested that NSCs alter the internal dynamics of the pre-existing hippocampal network and produce more specific responses to external inputs, thus enhancing the ability of the network to differentiate two different inputs.Significance.We demonstrated that NSCs improve the ability to distinguish external inputs by modulating the internal dynamics of a pre-existing network in a hippocampal culture. Our results provide novel insights into the relationship between NSCs and learning and memory.

Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective-direct and causal-connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.

Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration–a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration–determined from partial information decomposition–varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.

Networks compute information. That is, they modify inputs to generate distinct outputs. These computations are an important component of network information processing. Knowing how the routing of information in a network influences computation is therefore crucial. Here we asked how a key form of computation—synergistic integration—is related to the direction of local information flow in networks of spiking cortical neurons. Specifically, we asked how information flow between input neurons (i.e., recurrent information flow) and information flow from output neurons to input neurons (i.e., feedback information flow) was related to the amount of synergistic integration performed by output neurons. We found that greater synergistic integration occurred where there was more recurrent information flow. And, lesser synergistic integration occurred where there was more feedback information flow relative to feedforward information flow. These results show that computation, in the form of synergistic integration, is distinctly influenced by the directionality of local information flow. Such work is valuable for predicting where and how network computation occurs and for designing networks with desired computational abilities.

Culture of Rat Primary Cortical Cells for Microelectrode Array (MEA) Recordings to Screen for Acute and Developmental Neurotoxicity

Neurotoxicity testing of chemicals, drug candidates, and environmental pollutants still relies on extensive in vivo studies that are very costly, time-consuming, and ethically debated due to the large number of animals typically used. Currently, rat primary cortical cultures are widely used for in vitro neurotoxicity studies, as they closely resemble the in vitro brain with respect to the diversity of cell types, their physiological functions, and the pathological processes that they undergo. Common in vitro assays for neurotoxicity screening often focus on very target-specific endpoints such as morphological, biochemical, or electrophysiological changes, and such narrow focus can hamper translation and interpretation. Microelectrode array (MEA) recordings provide a non-invasive platform for extracellular recording of electrical activity of cultured neuronal cells, thereby enabling the evaluation of changes in neuronal (network) function as a sensitive and integrated endpoint for neurotoxicity screening. Here, we describe an in vitro approach for assessing changes in neuronal network function as a measure for neurotoxicity, using rat primary cortical cultures grown on MEAs. We provide a detailed protocol for the culture of rat primary cortical cells, and describe several experimental procedures to address acute, subchronic, and chronic exposure scenarios. We additionally describe the steps for processing and analyzing MEA and cell viability data. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Isolation and culture of rat primary cortical cells on 48-well MEA plates Support Protocol 1: Pretreatment and washing of 48-well MEA plates before first use or for re-use Support Protocol 2: Coating of 48-well MEA plates with 0.1% PEI solution Basic Protocol 2: MEA measurements during acute exposure Alternate Protocol 1: MEA measurements during subchronic exposure Alternate Protocol 2: MEA measurements during chronic exposure Support Protocol 3: Determination of cell viability after MEA experiments Basic Protocol 3: MEA data processing Basic Protocol 4: Analyzing MEA experiments after acute and subchronic exposure Alternate Protocol 3: Analyzing MEA experiments after chronic exposure.

Restoring neuronal chloride homeostasis with anti-NKCC1 gene therapy rescues cognitive deficits in a mouse model of Down syndrome

A common feature of diverse brain disorders is the alteration of GABA-mediated inhibition because of aberrant, intracellular chloride homeostasis induced by changes in the expression and/or function of chloride transporters. Notably, pharmacological inhibition of the chloride importer NKCC1 is able to rescue brain-related core deficits in animal models of these pathologies and in some human clinical studies. Here, we show that reducing NKCC1 expression by RNA interference in the Ts65Dn mouse model of Down syndrome (DS) restores intracellular chloride concentration, efficacy of gamma-aminobutyric acid (GABA)-mediated inhibition, and neuronal network dynamics in vitro and ex vivo. Importantly, adeno-associated virus (AAV)-mediated, neuron-specific NKCC1 knockdown in vivo rescues cognitive deficits in diverse behavioral tasks in Ts65Dn animals. Our results highlight a mechanistic link between NKCC1 expression and behavioral abnormalities in DS mice and establish a molecular target for new therapeutic approaches, including gene therapy, to treat brain disorders characterized by neuronal chloride imbalance.

Resilience of network activity in preconditioned neurons exposed to 'stroke-in-a-dish' insults

Exposing cultured cortical neurons to stimulatory agents - the K⁺ channel blocker 4-aminopyridine (4-ap), and the GABA_A receptor antagonist bicuculline (bic) - for 48 h induces down-regulated synaptic scaling, and preconditions neurons to withstand subsequent otherwise lethal 'stroke-in-a-dish' insults; however, the degree to which usual neuronal function remains is unknown. As a result, multi-electrode array and patch-clamp electrophysiological techniques were employed to characterize hallmarks of spontaneous synaptic activity over a 12-day preconditioning/insult experiment. Spiking frequency increased 8-fold immediately upon 4-ap/bic treatment but declined within the 48 h treatment window to sub-baseline levels that persisted long after washout. Preconditioning resulted in key markers of network activity - spiking frequency, bursting and avalanches - being impervious to an insult. Surprisingly, preconditioning resulted in higher peak NMDA mEPSC amplitudes, resulting in a decrease in the ratio of AMPA:NMDA mEPSC currents, suggesting a relative increase in synaptic NMDA receptors. An investigation of a broad mRNA panel of excitatory and inhibitory signaling mediators indicated preconditioning rapidly up-regulated GABA synthesis (GAD67) and BDNF, followed by up-regulation of neuronal activity-regulated pentraxin and down-regulation of presynaptic glutamate release (VGLUT1). Preconditioning also enhanced surface expression of GLT-1, which persisted following an insult. Overall, preconditioning resulted in a reduced spiking frequency which was impervious to subsequent exposure to 'stroke-in-a-dish' insults, a phenotype initiated predominantly by up-regulation of inhibitory neurotransmission, a lower neuronal postsynaptic AMPA: NMDA receptor ratio, and trafficking of GLT-1 to astrocyte plasma membranes.

Cytosolic PSD-95 interactor alters functional organization of neural circuits and AMPA receptor signaling independent of PSD-95 binding

Cytosolic PSD-95 interactor (cypin) regulates many aspects of neuronal development and function, ranging from dendritogenesis to synaptic protein localization. While it is known that removal of postsynaptic density protein-95 (PSD-95) from the postsynaptic density decreases synaptic N-methyl-D-aspartate (NMDA) receptors and that cypin overexpression protects neurons from NMDA-induced toxicity, little is known about cypin's role in AMPA receptor clustering and function. Experimental work shows that cypin overexpression decreases PSD-95 levels in synaptosomes and the PSD, decreases PSD-95 clusters/μm², and increases mEPSC frequency. Analysis of microelectrode array (MEA) data demonstrates that cypin or cypinΔPDZ overexpression increases sensitivity to CNQX (cyanquixaline) and AMPA receptor-mediated decreases in spike waveform properties. Network-level analysis of MEA data reveals that cypinΔPDZ overexpression causes networks to be resilient to CNQX-induced changes in local efficiency. Incorporating these findings into a computational model of a neural circuit demonstrates a role for AMPA receptors in cypin-promoted changes to networks and shows that cypin increases firing rate while changing network functional organization, suggesting cypin overexpression facilitates information relay but modifies how information is encoded among brain regions. Our data show that cypin promotes changes to AMPA receptor signaling independent of PSD-95 binding, shaping neural circuits and output to regions beyond the hippocampus.

Spontaneous synchronous network activity in the neonatal development of mPFC in mice

Spontaneous Synchronous Network Activity (SSA) is a hallmark of neurodevelopment found in numerous central nervous system structures, including neocortex. SSA occurs during restricted developmental time‐windows, commonly referred to as critical periods in sensory neocortex. Although part of the neocortex, the critical period for SSA in the medial prefrontal cortex (mPFC) and the underlying mechanisms for generation and propagation are unknown. Using Ca²⁺ imaging and whole‐cell patch‐clamp in an acute mPFC slice mouse model, the development of spontaneous activity and SSA was investigated at cellular and network levels during the two first postnatal weeks. The data revealed that developing mPFC neuronal networks are spontaneously active and exhibit SSA in the first two postnatal weeks, with peak synchronous activity at postnatal days (P)8–9. Networks remain active but are desynchronized by the end of this 2‐week period. SSA was driven by excitatory ionotropic glutamatergic transmission with a small contribution of excitatory GABAergic transmission at early time points. The neurohormone oxytocin desynchronized SSA in the first postnatal week only without affecting concurrent spontaneous activity. By the end of the second postnatal week, inhibiting GABA_A receptors restored SSA. These findings point to the emergence of GABA_A receptor‐mediated inhibition as a major factor in the termination of SSA in mouse mPFC.

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.

Presynaptic L-Type Ca2+ Channels Increase Glutamate Release Probability and Excitatory Strength in the Hippocampus during Chronic Neuroinflammation

Neuroinflammation is involved in the pathogenesis of several neurologic disorders, including epilepsy. Both changes in the input/output functions of synaptic circuits and cell Ca²⁺ dysregulation participate in neuroinflammation, but their impact on neuron function in epilepsy is still poorly understood. Lipopolysaccharide (LPS), a toxic byproduct of bacterial lysis, has been extensively used to stimulate inflammatory responses both in vivo and in vitro LPS stimulates Toll-like receptor 4, an important mediator of the brain innate immune response that contributes to neuroinflammation processes. Although we report that Toll-like receptor 4 is expressed in both excitatory and inhibitory mouse hippocampal neurons (both sexes), its chronic stimulation by LPS induces a selective increase in the excitatory synaptic strength, characterized by enhanced synchronous and asynchronous glutamate release mechanisms. This effect is accompanied by a change in short-term plasticity with decreased facilitation, decreased post-tetanic potentiation, and increased depression. Quantal analysis demonstrated that the effects of LPS on excitatory transmission are attributable to an increase in the probability of release associated with an overall increased expression of L-type voltage-gated Ca²⁺ channels that, at presynaptic terminals, abnormally contributes to evoked glutamate release. Overall, these changes contribute to the excitatory/inhibitory imbalance that scales up neuronal network activity under inflammatory conditions. These results provide new molecular clues for treating hyperexcitability of hippocampal circuits associated with neuroinflammation in epilepsy and other neurologic disorders.SIGNIFICANCE STATEMENT Neuroinflammation is thought to have a pathogenetic role in epilepsy, a disorder characterized by an imbalance between excitation/inhibition. Fine adjustment of network excitability and regulation of synaptic strength are both implicated in the homeostatic maintenance of physiological levels of neuronal activity. Here, we focused on the effects of chronic neuroinflammation induced by lipopolysaccharides on hippocampal glutamatergic and GABAergic synaptic transmission. Our results show that, on chronic stimulation with lipopolysaccharides, glutamatergic, but not GABAergic, neurons exhibit an enhanced synaptic strength and changes in short-term plasticity because of an increased glutamate release that results from an anomalous contribution of L-type Ca²⁺ channels to neurotransmitter release.

Loss of Snord116 alters cortical neuronal activity in mice: a preclinical investigation of Prader–Willi syndrome

Prader–Willi syndrome (PWS) is a neurodevelopmental disorder that is characterized by metabolic alteration and sleep abnormalities mostly related to rapid eye movement (REM) sleep disturbances. The disease is caused by genomic imprinting defects that are inherited through the paternal line. Among the genes located in the PWS region on chromosome 15 (15q11-q13), small nucleolar RNA 116 (Snord116) has been previously associated with intrusions of REM sleep into wakefulness in humans and mice. Here, we further explore sleep regulation of PWS by reporting a study with PWScrm+/p− mouse line, which carries a paternal deletion of Snord116. We focused our study on both macrostructural electrophysiological components of sleep, distributed among REMs and nonrapid eye movements. Of note, here, we study a novel electroencephalography (EEG) graphoelements of sleep for mouse studies, the well-known spindles. EEG biomarkers are often linked to the functional properties of cortical neurons and can be instrumental in translational studies. Thus, to better understand specific properties, we isolated and characterized the intrinsic activity of cortical neurons using in vitro microelectrode array. Our results confirm that the loss of Snord116 gene in mice influences specific properties of REM sleep, such as theta rhythms and, for the first time, the organization of REM episodes throughout sleep–wake cycles. Moreover, the analysis of sleep spindles present novel specific phenotype in PWS mice, indicating that a new catalog of sleep biomarkers can be informative in preclinical studies of PWS.

Familiarity Detection and Memory Consolidation in Cortical Assemblies

Humans have a large capacity of recognition memory (Dudai, 1997), a fundamental property of higher-order brain functions such as abstraction and generalization (Vogt and Magnussen, 2007). Familiarity is the first step towards recognition memory. We have previously demonstrated using unsupervised neural network simulations that familiarity detection of complex patterns emerges in generic cortical microcircuits with bidirectional synaptic plasticity. It is therefore meaningful to conduct similar experiments on biological neuronal networks to validate these results. Studies of learning and memory in dissociated rodent neuronal cultures remain inconclusive to date. Synchronized network bursts (SNBs) that occur spontaneously and periodically have been speculated to be an intervening factor. By optogenetically stimulating cultured cortical networks with random dot movies (RDMs), we were able to reduce the occurrence of SNBs, after which an ability for familiarity detection emerged: previously seen patterns elicited higher firing rates than novel ones. Differences in firing rate were distributed over the entire network, suggesting that familiarity detection is a system level property. We also studied the change in SNB patterns following familiarity encoding. Support vector machine (SVM) classification results indicate that SNBs may be facilitating memory consolidation of the learned pattern. In addition, using a novel network connectivity probing method, we were able to trace the change in synaptic efficacy induced by familiarity encoding, providing insights on the long-term impact of having SNBs in the cultures.

Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity

As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.g., bicuculline, BIC) in in vitro neuronal spike train data. Spike train data from recordings are likely to suffer from erroneous spike detection, such as missed spikes (false negative) or noise (false positive). Therefore, different timescale-dependent (cross-correlation, mutual information, spike time tiling coefficient) and timescale-independent (Spike-contrast, phase synchronization (PS), A-SPIKE-synchronization, A-ISI-distance, ARI-SPIKE-distance) synchrony measures were compared in terms of their robustness to erroneous spike trains. For this purpose, erroneous spike trains were generated by randomly adding (false positive) or deleting (false negative) spikes (in silico manipulated data) from experimental data. In addition, experimental data were analyzed using different spike detection threshold factors in order to confirm the robustness of the synchrony measures. All experimental data were recorded from cortical neuronal networks on microelectrode array chips, which show epileptiform activity induced by the substance BIC. As a result of the in silico manipulated data, Spike-contrast was the only measure that was robust to false-negative as well as false-positive spikes. Analyzing the experimental data set revealed that all measures were able to capture the effect of BIC in a statistically significant way, with Spike-contrast showing the highest statistical significance even at low spike detection thresholds. In summary, we suggest using Spike-contrast to complement established synchrony measures because it is timescale independent and robust to erroneous spike trains.

Functional Scaffolding for Brain Implants: Engineered Neuronal Network by Microfabrication and iPSC Technology

Neuroengineering methods can be effectively used in the design of new approaches to treat central nervous system and brain injury caused by neurotrauma, ischemia, or neurodegenerative disorders. During the last decade, significant results were achieved in the field of implant (scaffold) development using various biocompatible and biodegradable materials carrying neuronal cells for implantation into the injury site of the brain to repair its function. Neurons derived from animal or human induced pluripotent stem (iPS) cells are expected to be an ideal cell source, and induction methods for specific cell types have been actively studied to improve efficacy and specificity. A critical goal of neuro-regeneration is structural and functional restoration of the injury site. The target treatment area has heterogeneous and complex network topology with various types of cells that need to be restored with similar neuronal network structure to recover correct functionality. However, current scaffold-based technology for brain implants operates with homogeneous neuronal cell distribution, which limits recovery in the damaged area of the brain and prevents a return to fully functional biological tissue. In this study, we present a neuroengineering concept for designing a neural circuit with a pre-defined unidirectional network architecture that provides a balance of excitation/inhibition in the scaffold to form tissue similar to that in the injured area using various types of iPS cells. Such tissue will mimic the surrounding niche in the injured site and will morphologically and topologically integrate into the brain, recovering lost function.

The effect of acute glutamate treatment on the functional connectivity and network topology of cortical cultures

Microelectrode arrays (MEAs) allow the investigation of the pharmacological and toxicological effects of chemicals on cultured neuronal networks. Understanding the functional connections between neurons and the resulting neuronal networks is important for evaluating drugs that affect synaptic transmission. Therefore, we acutely treated a mature cultured neuronal network on MEAs with accumulating amounts of glutamate and recorded their altered electrophysiology. Subsequently, a cross-covariance analysis was applied to process the spiking activity in the network and to evaluate the connections between neurons. Finally, graph theory was used to assess the functional network properties under acute glutamate treatment. Our data demonstrated that glutamate increased the similarity, connectivity weight, density, and largest-component size of the functional network. In addition, the small-world network topology was altered after glutamate treatment. Our results indicate that the graph theory can advance our understanding of the pharmacological significance of neurotransmitters on neuronal networks.

Connectomics of Morphogenetically Engineered Neurons as a Predictor of Functional Integration in the Ischemic Brain

Recent advances in cell reprogramming technologies enable the in vitro generation of theoretically unlimited numbers of cells, including cells of neural lineage and specific neuronal subtypes from human, including patient-specific, somatic cells. Similarly, as demonstrated in recent animal studies, by applying morphogenetic neuroengineering principles in situ, it is possible to reprogram resident brain cells to the desired phenotype. These developments open new exciting possibilities for cell replacement therapy in stroke, albeit not without caveats. Main challenges include the successful integration of engineered cells in the ischemic brain to promote functional restoration as well as the fact that the underlying mechanisms of action are not fully understood. In this review, we aim to provide new insights to the above in the context of connectomics of morphogenetically engineered neural networks. Specifically, we discuss the relevance of combining advanced interdisciplinary approaches to: validate the functionality of engineered neurons by studying their self-organizing behavior into neural networks as well as responses to stroke-related pathology in vitro; derive structural and functional connectomes from these networks in healthy and perturbed conditions; and identify and extract key elements regulating neural network dynamics, which might predict the behavior of grafted engineered neurons post-transplantation in the stroke-injured brain.

Long-Term Activity Dynamics of Single Neurons and Networks

The firing rate of neuronal spiking in vitro and in vivo significantly varies over extended timescales, characterized by long-memory processes and complex statistics, and appears in spontaneous as well as evoked activity upon repeated stimulus presentation. These variations in response features and their statistics, in face of repeated instances of a given physical input, are ubiquitous in all levels of brain-behavior organization. They are expressed in single neuron and network response variability but even appear in variations of subjective percepts or psychophysical choices and have been described as stemming from history-dependent, stochastic, or rate-determined processes.But what are the sources underlying these temporally rich variations in firing rate? Are they determined by interactions of the nervous system as a whole, or do isolated, single neurons or neuronal networks already express these fluctuations independent of higher levels? These questions motivated the application of a method that allows for controlled and specific long-term activation of a single neuron or neuronal network, isolated from higher levels of cortical organization.This chapter highlights the research done in cultured cortical networks to study (1) the inherent non-stationarity of neuronal network activity, (2) single neuron response fluctuations and underlying processes, and (3) the interface layer between network and single cell, the non-stationary efficacy of the ensemble of synapses impinging onto the observed neuron.

In Vitro Models of Brain Disorders

The brain is the most complex organ of the body, and many pathological processes underlying various brain disorders are poorly understood. Limited accessibility hinders observation of such processes in the in vivo brain, and experimental freedom is often insufficient to enable informative manipulations. In vitro preparations (brain slices or cultures of dissociated neurons) offer much better accessibility and reduced complexity and have yielded valuable new insights into various brain disorders. Both types of preparations have their advantages and limitations with regard to lifespan, preservation of in vivo brain structure, composition of cell types, and the link to behavioral outcome is often unclear in in vitro models. While these limitations hamper general usage of in vitro preparations to study, e.g., brain development, in vitro preparations are very useful to study neuronal and synaptic functioning under pathologic conditions. This chapter addresses several brain disorders, focusing on neuronal and synaptic functioning, as well as network aspects. Recent progress in the fields of brain circulation disorders, excitability disorders, and memory disorders will be discussed, as well as limitations of current in vitro models.

Assessing functional connectivity across 3D tissue engineered axonal tracts using calcium fluorescence imaging

OBJECTIVE

Micro-tissue engineered neural networks (micro-TENNs) are anatomically-inspired constructs designed to structurally and functionally emulate white matter pathways in the brain. These 3D neural networks feature long axonal tracts spanning discrete neuronal populations contained within a tubular hydrogel, and are being developed to reconstruct damaged axonal pathways in the brain as well as to serve as physiologically-relevant in vitro experimental platforms. The goal of the current study was to characterize the functional properties of these neuronal and axonal networks.

APPROACH

Bidirectional micro-TENNs were transduced to express genetically-encoded calcium indicators, and spontaneous fluorescence activity was recorded using real-time microscopy at 20 Hz from specific regions-of-interest in the neuronal populations. Network activity patterns and functional connectivity across the axonal tracts were then assessed using various techniques from statistics and information theory including Pearson cross-correlation, phase synchronization matrices, power spectral analysis, directed transfer function, and transfer entropy.

MAIN RESULTS

Pearson cross-correlation, phase synchronization matrices, and power spectral analysis revealed high values of correlation and synchronicity between the spatially segregated neuronal clusters connected by axonal tracts. Specifically, phase synchronization revealed high synchronicity of  >0.8 between micro-TENN regions of interest. Normalized directed transfer function and transfer entropy matrices suggested robust information flow between the neuronal populations. Time varying power spectrum analysis revealed the strength of information propagation at various frequencies. Signal power strength was visible at elevated peak levels for dominant delta (1-4 Hz) and theta (4-8 Hz) frequency bands and progressively weakened at higher frequencies. These signal power strength results closely matched normalized directed transfer function analysis where near synchronous information flow was detected between frequencies of 2-5 Hz.

SIGNIFICANCE

To our knowledge, this is the first report using directed transfer function and transfer entropy methods based on fluorescent calcium activity to estimate functional connectivity of distinct neuronal populations via long-projecting, 3D axonal tracts in vitro. These functional data will further improve the design and optimization of implantable neural networks that could ultimately be deployed to reconstruct the nervous system to treat neurological disease and injury.

In vitro Cortical Network Firing is Homeostatically Regulated: A Model for Sleep Regulation

Prolonged wakefulness leads to a homeostatic response manifested in increased amplitude and number of electroencephalogram (EEG) slow waves during recovery sleep. Cortical networks show a slow oscillation when the excitatory inputs are reduced (during slow wave sleep, anesthesia), or absent (in vitro preparations). It was recently shown that a homeostatic response to electrical stimulation can be induced in cortical cultures. Here we used cortical cultures grown on microelectrode arrays and stimulated them with a cocktail of waking neuromodulators. We found that recovery from stimulation resulted in a dose-dependent homeostatic response. Specifically, the inter-burst intervals decreased, the burst duration increased, the network showed higher cross-correlation and strong phasic synchronized burst activity. Spectral power below <1.75 Hz significantly increased and the increase was related to steeper slopes of bursts. Computer simulation suggested that a small number of clustered neurons could potently drive the behavior of the network both at baseline and during recovery. Thus, this in vitro model appears valuable for dissecting network mechanisms of sleep homeostasis.

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions.

Simple and Inexpensive Paper-Based Astrocyte Co-culture to Improve Survival of Low-Density Neuronal Networks

Bottom-up neuroscience aims to engineer well-defined networks of neurons to investigate the functions of the brain. By reducing the complexity of the brain to achievable target questions, such in vitro bioassays better control experimental variables and can serve as a versatile tool for fundamental and pharmacological research. Astrocytes are a cell type critical to neuronal function, and the addition of astrocytes to neuron cultures can improve the quality of in vitro assays. Here, we present cellulose as an astrocyte culture substrate. Astrocytes cultured on the cellulose fiber matrix thrived and formed a dense 3D network. We devised a novel co-culture platform by suspending the easy-to-handle astrocytic paper cultures above neuronal networks of low densities typically needed for bottom-up neuroscience. There was significant improvement in neuronal viability after 5 days in vitro at densities ranging from 50,000 cells/cm² down to isolated cells at 1,000 cells/cm². Cultures exhibited spontaneous spiking even at the very low densities, with a significantly greater spike frequency per cell compared to control mono-cultures. Applying the co-culture platform to an engineered network of neurons on a patterned substrate resulted in significantly improved viability and almost doubled the density of live cells. Lastly, the shape of the cellulose substrate can easily be customized to a wide range of culture vessels, making the platform versatile for different applications that will further enable research in bottom-up neuroscience and drug development.

Theta rhythm-like bidirectional cycling dynamics of living neuronal networks in vitro

The phenomena of synchronization, rhythmogenesis and coherence observed in brain networks are believed to be a dynamic substrate for cognitive functions such as learning and memory. However, researchers are still debating whether the rhythmic activity emerges from the network morphology that developed during neurogenesis or as a result of neuronal dynamics achieved under certain conditions. In the present study, we observed self-organized spiking activity that converged to long, complex and rhythmically repeated superbursts in neural networks formed by mature hippocampal cultures with a high cellular density. The superburst lasted for tens of seconds and consisted of hundreds of short (50-100 ms) small bursts with a high spiking rate of 139.0 ± 78.6 Hz that is associated with high-frequency oscillations in the hippocampus. In turn, the bursting frequency represents a theta rhythm (11.2 ± 1.5 Hz). The distribution of spikes within the bursts was non-random, representing a set of well-defined spatio-temporal base patterns or motifs. The long superburst was classified into two types. Each type was associated with a unique direction of spike propagation and, hence, was encoded by a binary sequence with random switching between the two "functional" states. The precisely structured bidirectional rhythmic activity that developed in self-organizing cultured networks was quite similar to the activity observed in the in vivo experiments.

Quantification of Graph Complexity Based on the Edge Weight Distribution Balance: Application to Brain Networks

The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the ‘information’ (calculated by means of Shannon entropy) and the ‘order’ of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution. In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients. SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity. Our results showed that SGC is invariant with respect to GD and independent of node degree distribution. Furthermore, its variation with graph size [Formula: see text] is close to zero for [Formula: see text]. Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls. Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process. In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.

Functional characterization of GABAA receptor-mediated modulation of cortical neuron network activity in microelectrode array recordings

The numerous γ-aminobutyric acid type A receptor (GABA_AR) subtypes are differentially expressed and mediate distinct functions at neuronal level. In this study we have investigated GABA_AR-mediated modulation of the spontaneous activity patterns of primary neuronal networks from murine frontal cortex by characterizing the effects induced by a wide selection of pharmacological tools at a plethora of activity parameters in microelectrode array (MEA) recordings. The basic characteristics of the primary cortical neurons used in the recordings were studied in some detail, and the expression levels of various GABA_AR subunits were investigated by western blotting and RT-qPCR. In the MEA recordings, the pan-GABA_AR agonist muscimol and the GABA_BR agonist baclofen were observed to mediate phenotypically distinct changes in cortical network activity. Selective augmentation of αβγ GABA_AR signaling by diazepam and of δ-containing GABA_AR (δ-GABA_AR) signaling by DS1 produced pronounced changes in the majority of the activity parameters, both drugs mediating similar patterns of activity changes as muscimol. The apparent importance of δ-GABA_AR signaling for network activity was largely corroborated by the effects induced by the functionally selective δ-GABA_AR agonists THIP and Thio-THIP, whereas the δ-GABA_AR selective potentiator DS2 only mediated modest effects on network activity, even when co-applied with low THIP concentrations. Interestingly, diazepam exhibited dramatically right-shifted concentration-response relationships at many of the activity parameters when co-applied with a trace concentration of DS1 compared to when applied alone. In contrast, the potencies and efficacies displayed by DS1 at the networks were not substantially altered by the concomitant presence of diazepam. In conclusion, the holistic nature of the information extractable from the MEA recordings offers interesting insights into the contributions of various GABA_AR subtypes/subgroups to cortical network activity and the putative functional interplay between these receptors in these neurons.

Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony

BACKGROUND

Synchrony within neuronal networks is thought to be a fundamental feature of neuronal networks. In order to quantify synchrony between spike trains, various synchrony measures were developed. Most of them are time scale dependent and thus require the setting of an appropriate time scale. Recently, alternative methods have been developed, such as the time scale independent SPIKE-distance by Kreuz et al.

NEW METHOD

In this study, a novel time-scale independent spike train synchrony measure called Spike-contrast is proposed. The algorithm is based on the temporal "contrast" (activity vs. non-activity in certain temporal bins) and not only provides a single synchrony value, but also a synchrony curve as a function of the bin size.

RESULTS

For most test data sets synchrony values obtained with Spike-contrast are highly correlated with those of the SPIKE-distance (Spearman correlation value of 0.99). Correlation was lower for data containing multiple time scales (Spearman correlation value of 0.89). When analyzing large sets of data, Spike-contrast performed faster.

COMPARISON OF EXISTING METHOD

Spike-contrast is compared to the SPIKE-distance algorithm. The test data consisted of artificial spike trains with various levels of synchrony, including Poisson spike trains and bursts, spike trains from simulated neuronal Izhikevich networks, and bursts made of smaller bursts (sub-bursts).

CONCLUSIONS

The high correlation of Spike-contrast with the established SPIKE-distance for most test data, suggests the suitability of the proposed measure. Both measures are complementary as SPIKE-distance provides a synchrony profile over time, whereas Spike-contrast provides a synchrony curve over bin size.

Stimulation triggers endogenous activity patterns in cultured cortical networks

Cultures of dissociated cortical neurons represent a powerful trade-off between more realistic experimental models and abstract modeling approaches, allowing to investigate mechanisms of synchronized activity generation. These networks spontaneously alternate periods of high activity (i.e. network bursts) with periods of quiescence in a dynamic state which recalls the fluctuation of in vivo UP and DOWN states. Network bursts can also be elicited by external stimulation and their spatial propagation patterns tracked by means of multi-channel micro-electrode arrays. In this study, we used rat cortical cultures coupled to micro-electrode arrays to investigate the similarity between spontaneous and evoked activity patterns. We performed experiments by applying electrical stimulation to different network locations and demonstrated that the rank orders of electrodes during evoked and spontaneous events are remarkably similar independently from the stimulation source. We linked this result to the capability of stimulation to evoke firing in highly active and “leader” sites of the network, reliably and rapidly recruited within both spontaneous and evoked bursts. Our study provides the first evidence that spontaneous and evoked activity similarity is reliably observed also in dissociated cortical networks.

NETO1 Guides Development of Glutamatergic Connectivity in the Hippocampus by Regulating Axonal Kainate Receptors

Kainate-type glutamate receptors (KARs) are highly expressed in the developing brain, where they are tonically activated to modulate synaptic transmission, network excitability and synaptogenesis. NETO proteins are auxiliary subunits that regulate biophysical properties of KARs; however, their functions in the immature brain are not known. Here, we show that NETO1 guides the development of the rodent hippocampal CA3-CA1 circuitry via regulating axonal KARs. NETO deficiency reduced axonal targeting of most KAR subunits in hippocampal neurons in a subtype independent manner. As an interesting exception, axonal delivery of GluK1c was strongly and selectively impaired in the Neto1^−/−, but not Neto2^−/−, neurons. Correspondingly, the presynaptic GluK1 KAR activity that tonically inhibits glutamate release at immature CA3-CA1 synapses was completely lost in the absence of NETO1 but not NETO2. The deficit in axonal KARs at Neto1^−/− neurons resulted in impaired synaptogenesis and perturbed synchronization of CA3 and CA1 neuronal populations during development in vitro. Both these Neto1^−/− phenotypes were fully rescued by overexpression of GluK1c, emphasizing the role of NETO1/KAR complex in development of efferent connectivity. Together, our data uncover a novel role for NETO1 in regulation of axonal KARs and identify its physiological significance in development of the CA3-CA1 circuit.

Investigating the impact of electrical stimulation temporal distribution on cortical network responses

Background

The brain is continuously targeted by a wealth of stimuli with complex spatio-temporal patterns and has presumably evolved in order to cope with those inputs in an optimal way. Previous studies investigating the response capabilities of either single neurons or intact sensory systems to external stimulation demonstrated that stimuli temporal distribution is an important, if often overlooked, parameter.

Results

In this study we investigated how cortical networks plated over micro-electrode arrays respond to different stimulation sequences in which inter-pulse intervals followed a 1/f^β distribution, for different values of β ranging from 0 to ∞. Cross-correlation analysis revealed that network activity preferentially synchronizes with external input sequences featuring β closer to 1 and, in any case, never for regular (i.e. fixed-frequency) stimulation sequences. We then tested the interplay between different average stimulation frequencies (based on the intrinsic firing/bursting frequency of the network) for two selected values of β, i.e. 1 (scale free) and ∞ (regular). In general, we observed no preference for stimulation frequencies matching the endogenous rhythms of the network. Moreover, we found that in case of regular stimulation the capability of the network to follow the stimulation sequence was negatively correlated to the absolute stimulation frequency, whereas using scale-free stimulation cross-correlation between input and output sequences was independent from average input frequency.

Conclusions

Our results point out that the preference for a scale-free distribution of the stimuli is observed also at network level and should be taken into account in designing more efficient protocols for neuromodulation purposes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12868-017-0366-z) contains supplementary material, which is available to authorized users.

The Effects of Acute GABA Treatment on the Functional Connectivity and Network Topology of Cortical Cultures

γ-Aminobutyric acid (GABA) is an inhibitory transmitter, acting on receptor channels to reduce neuronal excitability in matured neural systems. However, electrophysiological responses of whole neuronal ensembles to the exposure to GABA are still unclear. We used micro-electrode arrays (MEAs) to study the effects of the increasing amount of GABA on functional network of cortical neural cultures. Then the recorded data were analyzed by the cross-covariance analysis and graph theory. Results showed that after the GABA treatment, the activity parameters of firing rate, bursting rate, bursting duration and network burst frequency in neural cultures decreased as expected. In addition, the functional connectivity also decreased in similarity, network density, and the size of the largest component. However, small-worldness was not found to be influenced by the acute GABA treatment. Our results support the position that using graph theory to evaluate the functional connectivity of neural cultures may enhance understanding of the pharmacological impact of neurotransmitters on neuronal networks.